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Journal articles on the topic 'Artificial Intelligence and Machine Learning (AI/ML)'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Srushti, Surendra Naik. "Understanding Artificial Intelligence and Machine Learning." International Journal of Advance and Applied Research S6, no. 22 (2025): 816–20. https://doi.org/10.5281/zenodo.15533444.

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<em>Artificial Intelligence (AI) and Machine Learning (ML) are the driving forces behind many technological advancements that are transforming our world. These fields are transforming how we approach problem-solving, decision-making, and interaction with technology. While AI generally refers to the simulation of human intelligence in machines, ML is a subset that focuses on enabling machines to learn and improve through experience.</em> <em>In this research paper, we will dive into the fundamentals of AI and ML, the various types of machine learning, the applications of AI and ML in various in

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Varma, Naredla Sathwika, Kalvakuntla Sharanya, and Dr D. Shravani. "AI Powered Virtual Assistant." International Journal of Future Engineering Innovations 2, no. 4 (2025): 51–54. https://doi.org/10.54660/ijfei.2025.2.4.51-54.

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Artificial intelligence (AI) and machine learning (ML) are two closely allied fields of computer science. Artificial intelligence (AI) is focused in developing intelligent machines; machine learning (ML) is a subfield of artificial intelligence (AI) aiming to enable machines to learn and grow from data without explicit programming. Machine learning methods enable data analysis and interpretation to identify trends and produce decisions or predictions. Combined artificial intelligence and machine learning has the ability to transform whole sectors, increase output, and improve our quality of li

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x, Rajdeep. "Mathematics Model Used in Artificial Intelligence (AI) and Machine Learning (ML)." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 1773–77. https://doi.org/10.21275/sr241227144834.

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Preet, Gandhi. "Artificial Intelligence and Machine Learning: Transforming the Future." International Journal of Advance and Applied Research S6, no. 22 (2025): 1215–17. https://doi.org/10.5281/zenodo.15543059.

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<em>Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological advancements, revolutionizing industries and reshaping human interactions with technology. AI enables machines to simulate human intelligence, while ML allows systems to learn from data and improve decision-making processes. This paper explores the fundamentals of AI and ML, their diverse applications, challenges, ethical considerations, and prospects for future development.</em>

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Lawlor, Bonnie. "Artificial Intelligence and Machine Learning." Chemistry International 43, no. 1 (2021): 8–13. http://dx.doi.org/10.1515/ci-2021-0103.

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Abstract The uses of Artificial Intelligence (AI) and Machine Learning (ML) are topics of presentations at most conferences today across diverse professional disciplines. Why? The following quote says it all:

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Naina, B. A., and M. D. Manoj. "Artificial Intelligence and Machine Learning Applications." International Journal of Advance and Applied Research 6, no. 25 (2025): 114–16. https://doi.org/10.5281/zenodo.15293993.

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<strong>Abstract:</strong> Automation, predictive analytics, and wise decision-making are some of the ways that artificial intelligence (AI) and machine learning (ML) are revolutionizing various industries.  Deep learning, natural language processing, and robotics are all included in AI, whereas machine learning improves performance through experience.  In the fields of healthcare, finance, manufacturing, and education, these technologies increase output, precision, and cost effectiveness.             AI helps in robotic-assisted

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Anika, Shreya Pawar. "Machine learning (ML) is an artificial intelligence (AI) technique." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 1422–25. https://doi.org/10.5281/zenodo.7073448.

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Machine learning (ML) is an artificial intelligence (AI) technique that facilitates the improvement of predictability in software applications without requiring explicit programming. Data from the past is used to predict new outcomes using machine learning algorithms. During the course of this paper, we used four machine learning algorithms: Logistic Regression, Support Vector Machine, Decision Tree, and Gradient Boosting. Our chosen algorithms were applied to five datasets from the healthcare domain, in which we were able to predict kidney disease, liver disease, breast cancer predictions, he

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Istamov, Mirjahon Mo'minjon o'g'li Mahkamov Baxtiyor Shuxratovich. "ARTIFICIAL INTELLIGENCE/MACHINE LEARNING IN DIABETES CARE." ilm-fan 1, no. 17 (2023): 75–77. https://doi.org/10.5281/zenodo.8141315.

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Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes care thereby making it more efficient. The huge burden of diabetes cases in India represents a unique set of problems, and provides us with a unique opportunity in terms of potential availability of data. Harnessing this data using electronic medical records, by all physicians, can put India at the forefront of research in this area. Application of AI/ML would provide insights to our problems as w

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Kapoor, Madhav. "Probabilistic Machine Learning and Artificial Intelligence." Spectrum of Emerging Sciences 3, no. 2 (2024): 29–36. http://dx.doi.org/10.55878/ses2023-3-2-5.

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Artificial intelligence (AI) and machine learning (ML) have transformed numerous domains by enabling systems to learn from data and make intelligent decisions. Within the field of ML, probabilistic machine learning has gained significant attention due to its ability to capture uncertainty and provide predictions based on probabilities. This essay explores the concept of probabilistic machine learning and its applications in AI. We delve into the fundamentals of machine learning, discuss probabilistic modeling, explore various probabilistic machine learning techniques, highlight the advantages

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Ojedokun, Samson Aderemi. "Artificial Intelligence (AI) and Machine Learning (ML); A Revolutionary Game Changer in Clinical Laboratory Diagnosis." Clinical Pathology & Research Journal 8, no. 1 (2024): 1–4. https://doi.org/10.23880/cprj-16000194.

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The first artificial intelligence (AI) program was developed by Christopher Strachey in 1951, though primitive. The term “Artificial Intelligence” was formed by John McCarthy at the Dartmouth Conference in 1956 marking the beginning of modern AI popularity

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Nisha, L. L. Josmin Laali. "ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) IN BIOLOGY." INTERNATIONAL JOURNAL OF PLANT BIOTECHNOLOGY 2, no. 1 (2025): 1–10. https://doi.org/10.34218/ijpbt_02_01_002.

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Shaumiwaty, Shaumiwaty, Mochamad Heru Riza Chakim, Heni Nurhaeni, and Victorianda. "Enhancing Personalized Learning Using Artificial Intelligence and Machine Learning Approaches." Blockchain Frontier Technology 4, no. 2 (2025): 156–70. https://doi.org/10.34306/bfront.v4i2.715.

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The convergence of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized the education landscape, shifting paradigms toward individualized and optimized learning environments. By harnessing AI predictive power and ML adaptive capabilities, educational outcomes are enhanced while equipping teachers with data driven insights for informed decision making. The primary objective of this research is to explore how customized learning environments, ML models, performance measurement, and AI algorithms improve educational outcomes and learning experiences. Despite the

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Kaur, Arshpreet. "Artificial Intelligence (AI) and Machine Learning (ML) for Sustainable Agriculture." International Journal of Research and Scientific Innovation XII, no. V (2025): 470–76. https://doi.org/10.51244/ijrsi.2025.12050040.

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Agriculture is backbone of our nation’s economy. It is the main source of livelihood for about the majority of the population of India, particularly in rural areas. With the advancement of technology this sector is revolutionizing at fast speed.Artificial intelligence is playing a vital role in transformation of agriculture to Smart Agriculture. Artificial intelligence (AI) uses various sub-domains as Machine Learning(ML) ,Deep learning(DL), Internet Of Things (IoT), Big Data etc to enhance the production of agriculture. With growing population, It is essential to increase the productivity of

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Sunarya, Po Abas. "Machine Learning and Artificial Intelligence as Educational Games." International Transactions on Artificial Intelligence (ITALIC) 1, no. 1 (2022): 129–38. http://dx.doi.org/10.34306/italic.v1i1.206.

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Digital games are establishing themselves as a new paradigm in teaching. Everyone can play digital games, they're economical, and they're a terrific way to learn. Digital games that encourage computational thinking and programming have recently attracted more attention in pre-college (K–12) educational institutions. A growing number of students have been drawn to the subjects of In recent years, there has been an increase in artificial intelligence (AI) and machine learning (ML). Researchers in teaching and learning are interested in the integration of AI/ML with digital gaming, however there

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Sunarya, Po Abas. "Machine Learning and Artificial Intelligence as Educational Games." International Transactions on Artificial Intelligence (ITALIC) 1, no. 1 (2022): 129–38. http://dx.doi.org/10.33050/italic.v1i1.206.

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Digital games are establishing themselves as a new paradigm in teaching. Everyone can play digital games, they're economical, and they're a terrific way to learn. Digital games that encourage computational thinking and programming have recently attracted more attention in pre-college (K–12) educational institutions. A growing number of students have been drawn to the subjects of In recent years, there has been an increase in artificial intelligence (AI) and machine learning (ML). Researchers in teaching and learning are interested in the integration of AI/ML with digital gaming, however there

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Asiri, Saeed N., Larry P. Tadlock, Emet Schneiderman, and Peter H. Buschang. "Applications of artificial intelligence and machine learning in orthodontics." APOS Trends in Orthodontics 10 (March 30, 2020): 17–24. http://dx.doi.org/10.25259/apos_117_2019.

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Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment p

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Kandragula, Srikanth. "Machine Learning and Artificial Intelligence in Cloud Computing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–4. http://dx.doi.org/10.55041/ijsrem17662.

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The field of technology is constantly evolving, and at the forefront of this progress lies the powerful synergy between cloud computing and machine learning (ML). Cloud computing provides a robust and scalable platform that serves as the launchpad for advancements in artificial intelligence (AI), particularly machine learning. This platform offers features that empower ML development, including access to vast and scalable resources, cost-effective solutions, collaborative tools, and global reach. Machine learning, in turn, becomes the engine that propels cloud applications forward, enabling th

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Chowdhury, Wazahat Ahmed. "Optimizing Supply Chain Logistics Through AI & ML: Lessons from NYX." International journal of data science and machine learning 05 (April 20, 2025): 49–53. https://doi.org/10.55640/ijdsml-05-01-10.

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Modern day Supply chain and logistics management system integrates artificial intelligence (AI) and machine learning (ML) to develop it into an operational transformation which enhances resilience, reduces costs and improves efficiency in corporate offices. This paper evaluates how artificial intelligence and machine learning-based demand forecasting and route optimization systems facilitate process optimization through inventory management. This paper applies to NYX as an example of a mid-sized logistics manufacturer to present real-world applications of these technologies and extract importa

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Chander Diwaker and Atul Sharma. "Significance of Artificial Intelligence and Machine Learning in Business Enterprises." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1093–98. https://doi.org/10.32628/cseit2410467.

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The rising demand for Artificial Intelligence (AI) and Machine Learning (ML) technologies in the corporate sector offers significant opportunity for firms to foster innovation and change. Incorporating generative AI capabilities into AI/ML corporate adoption plans can significantly improve the performance and efficacy of these technologies. By adopting AI/ML with generative AI functionalities, organizations may optimize content production workflows, provide synthetic data to enhance ML model training, and provide more captivating consumer experiences. This connection enhances creativity and in

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Tiwari, Tanya, Tanuj Tiwari, and Sanjay Tiwari. "How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?" International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i2.569.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so

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Shayari, Dutta, and Kumar Chaudhuri Tapan. "Artificial Intelligence and Machine Learning- Driven Pharmaceutical Industry." International Journal of Current Science Research and Review 08, no. 05 (2025): 2118–25. https://doi.org/10.5281/zenodo.15387287.

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Abstract : Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the pharmaceutical sector at every stage—drug discovery, development, regulatory affairs, quality control, and post-marketing surveillance. These technologies improve data processing, accuracy, and timelines by using complex algorithms and large volumes of healthcare data. AI helps in drug target identification, drug design, prediction of toxicity, and pharmacokinetics modeling, as well as improving regulatory processes and pharmacovigilance. Though they have their benefits, there are still challenges s

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Sharma, Roy Kshemendra. "Artificial Intelligence, Machine Learning and the Reconstruction of Employee Psychology." NHRD Network Journal 13, no. 4 (2020): 472–79. http://dx.doi.org/10.1177/2631454120971912.

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Artificial intelligence (AI) and machine learning (ML) are opening up important avenues of value creation inside organisations. Value creation will be strengthened if AI and ML are conceptualised using theoretical anchors rooted in a strong understanding of employee psychology. Organisations need to address anxieties of employees and assure them that AI and ML are aids in improving their productivity. They need to communicate to employees that AI and ML are not meant to displace employees from their jobs. Organisations face another dilemma regarding how to use AI and ML to draw upon the tacit

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Yadav, Narendra, Latika Sharma, and Urmila Dhake. "Artificial Intelligence: The Future." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27796.

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Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. "AI" may also refer to the machines themselves. AI is not a new for the scientist, it was introduce in 1943 with artificial neurons model and get popular in 1950 due to “Turting test” the test was done to get answer that machine can think?, purposed by Alan Turing. Basically AI is categorized into three types, Artificial Narrow Intelligence, Artificial General Intelligence and

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Wang, Renjie, Wei Pan, Lei Jin, et al. "Artificial intelligence in reproductive medicine." Reproduction 158, no. 4 (2019): R139—R154. http://dx.doi.org/10.1530/rep-18-0523.

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Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine

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Reddy, Dr P. Vijaya Vardhan. "An Apt Process for Machine Learning and Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 819–25. http://dx.doi.org/10.22214/ijraset.2024.59920.

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Abstract: The paper presents an apt process for Machine Learning and Artificial Intelligence. To solve the real-world problems with Machine Learning (ML) and Artificial Intelligence (AI) a definite process is required which would emphasis on complete solution rather than approaching as randomly. Machine Learning and Artificial Intelligence are not just mere algorithms which you can put anywhere and start getting fantabulous results. ML and AI are processes which starts with defining the data and completes with the model with defined level of accuracy. In this paper an apt process is proposed t

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Janhavi, Baikar. "Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics: A Review." International Journal of Advance and Applied Research S6, no. 22 (2025): 1052–57. https://doi.org/10.5281/zenodo.15534510.

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<em>Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments. Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots) that can work al

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Wassima, LAKHCHINI, WAHABI Rachid, and EL KABBOURI Mounime. "Artificial Intelligence & Machine Learning in Finance: A literature review." International Journal of Accounting, Finance, Auditing, Management and Economics 3, no. 6-1 (2022): 437–55. https://doi.org/10.5281/zenodo.7454232.

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In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five

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Kasthurirengan, Arulmozhi. "Analyzing the Role of AI/ML in Optimizing Retail." International Scientific Journal of Engineering and Management 02, no. 05 (2023): 1–8. https://doi.org/10.55041/isjem00692.

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The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources, preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) pl

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Sonali, Chaurasia, A. Waoo Ashwini, and Akhilesh A. Waoo Dr. "Artificial Intelligence and Applied Machine Learning for Climate Change." International Journal of Contemporary Research in Multidisciplinary 4, S2 (2025): 43–46. https://doi.org/10.5281/zenodo.15177111.

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Artificial Intelligence (AI) is believed to have significant potential use in tackling. Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help in terms of applied climate change. A subset of artificial intelligence (AI), machine learning focuses on the development of systems that can learn from past data for climate change in the platform of AI. There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. Here, we introduce a systematic framework for describing the effects of m

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Naresh, Lokiny. "The Role of AI and Machine Learning in DevOps Automation." Journal of Scientific and Engineering Research 7, no. 2 (2020): 228–33. https://doi.org/10.5281/zenodo.13348137.

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This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in DevOps automation. The integration of AI and ML technologies in DevOps practices has revolutionized the software development lifecycle, enabling organizations to achieve faster delivery, improved quality, and enhanced efficiency. By leveraging AI and ML algorithms, DevOps teams can automate repetitive tasks, predict potential issues, and optimize workflows to drive continuous improvement. This paper delves into the significance of AI and ML in DevOps automation, highlighting key benefits, c

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Researcher. "ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: CURRENT DEVELOPMENTS AND FUTURE PROSPECTS." International Journal of Artificial Intelligence & Machine Learning (IJAIML) 3, no. 2 (2024): 163–72. https://doi.org/10.5281/zenodo.13752915.

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Artificial Intelligence (AI) and Machine Learning (ML) have undergone remarkable progress in the past ten years, resulting in profound effects on multiple sectors such as healthcare, finance, and transportation. This research paper provides an in-depth analysis of the current advancements in AI and ML, explores the latest technologies and methodologies, and discusses future directions and challenges. Through a detailed examination of contemporary trends and illustrative case studies, this paper seeks to provide a thorough understanding of the evolution of AI and ML, as well as their potential

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Salunke, Aryan. "Road Accident Detection Using AI and ML." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48619.

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Abstract - Road accidents have emerged as a critical global concern, resulting in significant loss of life, injuries, and property damage. Timely detection and response to such incidents can significantly reduce fatalities and improve emergency services. In this research, we propose an intelligent road accident detection system utilizing Artificial Intelligence (AI) and Machine Learning (ML) approaches. The system employs advanced computer vision techniques integrated with real-time video surveillance for accurate accident detection. Deep Learning models, particularly Convolutional Neural Netw

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Babu,, M. A. Suresh. "Applications of Artificial Intelligence in (Machine Learning /Deep learning) Smart Grid." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42202.

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Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), plays a significant role in enhancing the efficiency, reliability, and sustainability of smart grids. One of its key applications is load forecasting and demand response, where Machine Learning models predict electricity demand based on historical consumption patterns, weather conditions, and economic factors - helping in real-time energy optimization. AI also enables renewable energy integration, grid fault detection and maintenance, energy theft detection, voltage and frequency stability control, AI pred

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Kambala, Mahesh. "AI-Powered Healthcare: Transforming Patient Outcomes with Machine Learning." Journal of Medical Science and clinical Research 12, no. 08 (2024): 34–47. http://dx.doi.org/10.18535/jmscr/v12i08.07.

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AI and ML have flooded the healthcare industry with new technological approaches to affect patient experiences through smart approaches towards predictability, treatment, and diagnosis. The following paper focuses on exploring the effects caused by the implementation of artificial intelligence technologies in the sphere of healthcare. This research explores different case studies to prove that early diagnosis, treatment customization, and organizational effectiveness are all driven by AI. The paper is concerned with the approaches used in the implementation of artificial intelligence in the he

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K, Manisha, Bhole Bhole, and Dr Pankaj H. Zope. "Review on Advanced Automation Using Artificial Intelligence, Machine Learning and Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42425.

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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of automation and advanced robotics in recent years. AI, ML, and Deep Learning are transforming the field of automation, making robots more intelligent, eﬃcient, and adaptable to complex tasks and environments. Some of the applications of AI, ML, and Deep Learning include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots) that can work

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Soni, Akhil Chandra. "Enhancing Trip Planning with Undetectable AI and Machine Learning Personalization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35294.

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This research investigates the influence of Artificial Intelligence (AI) and Machine Learning (ML) on the personalization of travel itineraries. The study explores how these technologies enhance user experience, streamline planning processes, and address the complexities of trip customization. Emphasis is placed on the methods employed to ensure AI-driven solutions remain undetectable to users, maintaining a seamless and intuitive interface. Keywords: AI –Artificial Intelligence, ML-Machine Learning, Personalised Itinerary

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El Qasemy, Hajar. "Cognitive Technologies: Machine Learning, Artificial Intelligence, and Convolutional Neural Networks in Computer Vision." Westcliff International Journal of Applied Research 9, no. 1 (2025): 5–17. https://doi.org/10.47670/wuwijar20251heq.

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The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, repo

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Sundaram, Karthik Trichur. "Digital Transformation with AI/ML & Cybersecurity." International Journal of Computer Science and Mobile Computing 11, no. 11 (2022): 1–3. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.001.

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Artificial Intelligence (AI) and Machine Learning (ML) have impacted the manufacturing industry, especially in the industry 4.0 paradigm. It encourages the usage of smart devices, sensors, and machines for production. Moreover, AI techniques and ML algorithms give predictive insights into various manufacturing tasks, such as predictive maintenance, continuous inspection, process optimization, quality improvement, and more. However, there are many open concerns and challenges regarding cybersecurity in smart manufacturing.

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Vempuluru, Vijitha S., Gaurav Patil, Rajiv Viriyala, Krishna K. Dhara, and Swathi Kaliki. "Artificial intelligence and machine learning in ocular oncology, retinoblastoma (ArMOR)." Indian Journal of Ophthalmology 73, no. 5 (2025): 741–43. https://doi.org/10.4103/ijo.ijo_1768_24.

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Purpose: To test the accuracy of a trained artificial intelligence and machine learning (AI/ML) model in the diagnosis and grouping of intraocular retinoblastoma (iRB) based on the International Classification of Retinoblastoma (ICRB) in a larger cohort. Methods: Retrospective observational study that employed AI, ML, and open computer vision techniques. Results: For 1266 images, the AI/ML model displayed accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 95%, 94%, 98%, 99%, and 80%, respectively, for the detection of RB. For 173 eyes, t

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Gupta, Adhyayan. "Machine Learning and Deep Learning: A Comprehensive Overview." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1620–26. https://doi.org/10.22214/ijraset.2025.72470.

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Machine Learning (ML) and Deep Learning (DL) are two core areas of Artificial Intelligence (AI) that have significantly transformed technology and research. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, te

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Nitin, Liladhar Rane, Paramesha Mallikarjuna, P. Choudhary Saurabh, and Rane Jayesh. "Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: A Review." Partners Universal International Innovation Journal (PUIIJ) 02, no. 03 (2024): 147–71. https://doi.org/10.5281/zenodo.12208298.

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This study thoroughly analyses how artificial intelligence (AI), machine learning (ML), and deep learning (DL) impact the development and improvement of business strategies. It examines how AI changes business models, highlighting its ability to stimulate innovation, improve procedural effectiveness, and enhance decision-making abilities. The discussion explores the numerous uses of ML algorithms, including predicting market trends, customizing consumer engagements, and enhancing logistic systems. Moreover, it explores the use of DL techniques to analyse extensive amounts of unstructured data,

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Koulaouzidis, George, Tomasz Jadczyk, Dimitris K. Iakovidis, Anastasios Koulaouzidis, Marc Bisnaire, and Dafni Charisopoulou. "Artificial Intelligence in Cardiology—A Narrative Review of Current Status." Journal of Clinical Medicine 11, no. 13 (2022): 3910. http://dx.doi.org/10.3390/jcm11133910.

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Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s com

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Bansal, Devasheesh. "Understanding Smart Machines: A Holistic Study of AI and ML Concepts, Learning Algorithms and Cross-Industry Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 4266–71. https://doi.org/10.22214/ijraset.2025.71141.

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Machine learning ML and artificial intelligence AI have revolutionized industries, driving advancements in automation, decision making, and data driven insights. This paper provides a review of machine learning and artificial intelligence, covering fundamental concepts, algorithms, mathematical foundations, real world applications, and challenges

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VD, Bhargavi, Jayavarshini JV, Dr V. Rameshbabu, and Dr T. V. Ananathan. "Hoax Detector Using Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43481.

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The rise of misinformation and hoaxes on the internet poses a significant threat to public trust and safety. This project presents a Hoax Detection system leveraging Artificial Intelligence (AI) and web scraping techniques to identify and flag potential hoaxes or false information across various online platforms. The system employs Natural Language Processing (NLP) algorithms to analyze text patterns and classify content as likely hoaxes or credible information. By integrating Machine Learning (ML) models trained on datasets of verified and unverified information, the system learns to distingu

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Siddique, Sarkar, and James C. L. Chow. "Machine Learning in Healthcare Communication." Encyclopedia 1, no. 1 (2021): 220–39. http://dx.doi.org/10.3390/encyclopedia1010021.

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare c

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Priyanka, Shaveta Azad, and Rupak Chakravarty. "Artificial intelligence (AI) literature in patents: a global landscape." LIBRARY HI TECH NEWS 38, no. 07 (2021): 24–28. https://doi.org/10.1108/LHTN-09-2021-0062.

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Artificial intelligence (AI) was first coined decades ago in the year 1956 by John McCarthy at Dartmouth conference; he defines AI is the science and engineering of making intelligent machines; in a sense AI is a technique of getting machine to work and behave similar to humans. In recent past, AI has been able to accomplish this by creating machines and robots that have been used in a wide range of fields including health care, robotics, marketing, business analytics and many more. However, many applications are not perceived as AI because user often tend to think of AI as robots doing our da

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CUNHA, Maria Nascimento, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, and Isabel OLIVEIRA. "REDEFINING CONSUMER ENGAGEMENT: THE IMPACT OF AI AND MACHINE LEARNING ON MARKETING STRATEGIES IN TOURISM AND HOSPITALITY." GeoJournal of Tourism and Geosites 53, no. 2 (2024): 514–21. http://dx.doi.org/10.30892/gtg.53214-1226.

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This article aims to systematically investigate and elucidate the transformative effects of Artificial Intelligence (AI) and Machine Learning (ML) on marketing strategies and consumer engagement within the tourism and hospitality industries. The research methodology employed in this article encompasses a quantitative approach, underpinned by the use of cluster analysis to categorize and interpret complex multivariate data. This methodological framework is chosen to provide a rigorous, data-driven examination of the impacts of Artificial Intelligence (AI) and Machine Learning (ML) on marketing

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Rajitha, Akula, Aravinda K, Amandeep Nagpal, et al. "Machine Learning and AI-Driven Water Quality Monitoring and Treatment." E3S Web of Conferences 505 (2024): 03012. http://dx.doi.org/10.1051/e3sconf/202450503012.

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This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their

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Sugali, Kishore, Chris Sprunger, and Venkata N Inukollu. "Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning." International Journal of Artificial Intelligence & Applications 12, no. 1 (2021): 101–12. http://dx.doi.org/10.5121/ijaia.2021.12107.

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The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing

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Ramoo, Vimala. "Advancing Critical Care Nursing: Navigating Artificial Intelligence (AI) and Machine Learning (Ml)." International Journal of Critical Care 18, no. 4 (2025): 33–34. https://doi.org/10.29173/ijcc977.

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As the healthcare sector stands on the brink of a technological revolution, critical care nursing faces the imperative and challenging task of navigating the integration of Artificial Intelligence (AI) and Machine Learning (ML) into its practices. This presentation delves into the transformative journey of incorporating these advanced technologies to enhance patient care, optimize workflows, and address the complexities of critical care environments. AI and ML are not just tools for innovation but are becoming essential components in the evolution of nursing care. They offer sophisticated solu

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